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A little over a year ago, on February 17, 2017, the Houston Chronicle reported that the University of Texas’ MD Anderson Cancer Center had halted an AI project for cancer diagnostics. The story revealed that MD Anderson spent $62 million over four years to build a system called the Oncology Expert Advisor (OEA), based on IBM Watson. As envisioned by its champions,

This appeared in my Twitter feed recently: Challenge…accepted. You can find a copy of SAS’ report here, and Matt Asay’s excellent analysis here. Grab your popcorn; here are four quick points. (1) For SAS, it’s progress. The report opens with this: Open source technologies, like Hadoop, R, and Python, have been vital to the spread of big data. That’s quite an

Forrester recently published the Wave™ for Predictive Analytics And Machine Learning Solutions. You can purchase a copy from Forrester for $2,495, or get a free copy here. When Forrester last delivered this analysis in 2015, they called it the Wave™ for Big Data Predictive Analytics Solutions. So, I guess that Big Data is “out” and machine learning is “in.” The chart below shows the

It’s only March, but already IBM leads the software industry in gasbaggery. Gartner’s most recent Magic Quadrant for Data Science Platforms includes this little gem: Customers are often confused by mismatches between (IBM’s) marketing messages and actual, purchasable products. That’s a polite way to say that IBM marketing messages have enough hot air to float a fleet of balloons over the Bernese

Gartner recently released its 2017 Magic Quadrant for Data Science Platforms. You can get a copy directly from Gartner if you’re a client, or you can get one for free here, courtesy of SAS. The figure below shows the 2016 and 2017 MQs side by side, with changes shown in red. Here are my comments about the 2016 MQ, written last

At the 2016 Spark Summit, Gartner Research Director Nick Heudecker asked: Is Spark the Future of Data Analysis? It’s an interesting question, and it requires a little parsing. Nobody believes that Spark alone is the future of data analysis, even its most ardent proponents. A better way to frame the question: Does Spark have a role in the future of analytics?

As an addendum to my year-end review of machine learning and deep learning, I offer this survey of SQL engines. SQL is the most widely used language for data science according to O’Reilly’s 2016 Data Science Salary Survey. Most projects require at least some SQL operations, and many need nothing but SQL. This review covers six open source leaders: Hive, Impala,